1. The Field of the Invention
The present invention relates to roadway traffic monitoring, and more particularly, to determining the presence and location of vehicles traveling upon a multilane roadway.
2. The Relevant Technology
Vehicular traffic monitoring continues to be of great public interest since derived statistics are valuable for determination of present traffic planning and conditions as well as providing statistical data for facilitating more accurate and reliable urban planning. With growing populations, there is increasing need for current and accurate traffic statistics and information. Useful traffic information requires significant statistical gathering of traffic information and careful and accurate evaluation of that information. Additionally, the more accurate and comprehensive the information, such as vehicle density per lane of traffic, the more sophisticated the planning may become.
Roadway traffic surveillance has relied upon measuring devices, which have traditionally been embedded into the road, for both measuring traffic conditions and providing control to signaling mechanisms that regulate traffic flow. Various sensor technologies have been implemented, many of which have been xe2x80x9cin-pavementxe2x80x9d types. In-pavement sensors include, among others, induction loops which operate on magnetic principles. Induction loops, for example, are loops of wire which are embedded or cut into the pavement near the center of a pre-defined lane of vehicular traffic. The loop of wire is connected to an electrical circuit that registers a change in the inductance of the loops of wire when a large metallic object, such as a vehicle, passes over the loops of wire embedded in the pavement. The inductance change registers the presence of a vehicle or a count for the lane of traffic most closely associated with the location of the induction loops.
Induction loops and other in-pavement sensors are unreliable and exhibit a high failure rate due to significant mechanical stresses caused by the pavement forces and weather changes. Failures of loops are common and it has been estimated that at any one time, 20%-30% of all installed controlled intersection loops are non-responsive.
Furthermore, the cost to repair these devices can be greater than the original installation cost.
Installation and repair of in-pavement sensors also require significant resources to restrict and redirect traffic during excavation and replacement and also present a significant risk to public safety and inconvenience due to roadway lane closures which may continue for several hours or days. Interestingly, some of these technologies have been employed for over sixty years and continue to require the same amount of attention in installation, calibration, maintenance repair and replacement as they did several decades ago. This can be due to a number of factors from inferior product design or poor installation to post installation disruption or changing traffic flow patterns. Subsequently this technology can be extremely costly and inefficient to maintain as an integral component to an overall traffic plan.
To their credit, traffic control devices serve the interest of public safety, but in the event of a new installation, or maintenance repair, they act as a public nuisance, as repair crews are required to constrict or close multiple lanes of traffic for several hours to reconfigure a device or even worse, dig up the failed technology for replacement by closing one or more lanes for several days or weeks. Multiple lane closures are also unavoidable with embedded sensor devices that are currently available when lane reconfiguration or re-routing is employed. Embedded sensors that are no longer directly centered in a newly defined lane of traffic may miss vehicle detections or double counts a single vehicle. Such inaccuracies further frustrate the efficiency objectives of traffic management, planning, and control.
Such complications arise because inductive loop sensors are fixed location sensors, with the limitation of sensing only the traffic that is immediately over them. As traffic patterns are quite dynamic and lane travel can reconfigure based on stalled traffic, congestion, construction/work zones and weather, the inductive loop is limited in its ability to adapt to changing flow patterns and is not able to reconfigure without substantial modification to its physical placement.
Several non-embedded sensor technologies have been developed for traffic monitoring. These include radar-based sensors, ultrasound sensors, infrared sensors, and receive-only acoustic sensors. Each of these new sensory devices has specific benefits for traffic management, yet none of them can be reconfigured or adapted without the assistance of certified technicians. Such an on-site modification to the sensors may require traffic disruptions and may take several hours to several days for a single intersection reconfiguration.
Another traffic monitoring technology includes video imaging which utilizes intersection or roadside cameras to sense traffic based on recognizable automobile characteristics (e.g.; headlamps, bumper, windshield, etc.). In video traffic monitoring, a camera is manually configured to analyze a specific user-defined zone within the camera""s view. The user-defined zone remains static and, under ideal conditions may only need to be reconfigured with major intersection redesign. As stated earlier, dynamic traffic patterns almost guarantee that traffic will operate outside the user defined zones, in which case, the cameras will not detect actual traffic migration. Furthermore, any movement in the camera from high wind to gradual movement in the camera or traffic lanes over time will affect the camera""s ability to see traffic within its user-defined zone. In order to operate as designed, such technology requires manual configuration and reconfiguration.
Another known technology alluded to above includes acoustic sensors which operate as traffic listening devices. With an array of microphones built into the sensor, the acoustic device is able to detect traffic based on spatial processing changes in sound waves as the sensor receives them. Detection and traffic flow information are then assigned to the appropriate user-defined lane being monitored. This technology then forms a picture of the traffic based on the listening input, and analyzes it based on user assigned zones. Again, once the sensor is programmed, it will monitor traffic flow within the defined ranges only under ideal conditions.
Like an imaging camera, the acoustic sensor can hear traffic noise in changing traffic patterns, but it will only be monitored if it falls within the pre-assigned zone. Unable to reconfigure during changes in the traffic pattern, the acoustic sensor requires on-site manual reconfiguration in order to detect the new traffic flow pattern. In an acoustic sensor, microphone sensitivity is typically pre-set at a normal operating condition, and variations in weather conditions can force the noise to behave outside those pre-set ranges.
Yet another traffic sensor type is the radar sensor which transmits a low-power microwave signal from a source mounted off-road in a xe2x80x9cside-firexe2x80x9d configuration or perpendicular angle transmitting generally perpendicular to the direction of traffic. In a sidefire configuration, a radar sensor is capable of discriminating between multiple lanes of traffic. The radar sensor detects traffic based on sensing the reflection of transmitted radar. The received signal is then processed and, much like acoustic sensing, detection and traffic flow information are then assigned to the appropriate user-defined lane being monitored. This technology then forms a picture of the traffic based on the input, and analyzes it based on user-assigned zones. Under ideal conditions, once these zones are manually set, they are monitored as the traffic flow operates within the pre-set zones. Consequently, any change in the traffic pattern outside those predefined zones needs to be manually reset in order to detect and monitor that zone.
As discussed above, several sensors may be employed to identify multiple lanes of vehicular traffic. While sensors may be positioned to detect passing traffic, the sensors must be configured and calibrated to recognize specific traffic paths or lanes. Consequently, such forms of detection sensors require manual configuration when the system is deployed and manual reconfiguration when traffic flow patterns change. Furthermore, temporary migration of traffic lanes, such as during, for example, a snow storm or construction re-routing, results in inaccurate detection and control. Without reconfiguration, the devices may continue to sense, but they may discard the actual flow pattern as peripheral noise, and only count the traffic that actually appears in their user-defined zones. The cost to configure and reconfigure devices can be considerable, and disruption to traffic is unavoidable under any circumstance. Furthermore, inaccurate counting of traffic flow can result in improper and even unsafe traffic control and inaccurate and inconvenient traffic reporting.
Thus, there exists a need for a method and system for configuring and continuously reconfiguring traffic sensors according to current traffic flow paths thereby enabling improved traffic control, traffic planning and enhanced public safety and convenience without requiring constant manual evaluation and intervention.
A traffic monitoring system which employs a sensor for monitoring traffic conditions about a roadway or intersection is presented. As roadways exhibit traffic movement in various directions and across various lanes, the sensor detects vehicles passing through a field of view. The sensor data is input into a Fourier transform algorithm to convert from the time domain signal into the frequency domain. Each of the transform bins exhibits the respective energies with ranging being proportional to the frequency. A detection threshold discriminates between vehicles and other reflections.
A vehicle position is estimated as the bin in which the peak of the transform is located. A detection count is maintained for each bin and contributes to the probability density function estimation of vehicle position. The probability density function describes the probability that a vehicle will be located at any range. The peaks of the probability function represent the center of each lane and the valleys of the probability density function represent the lane boundaries. The boundaries are then represented with each lane being defined by multiple range bins with each range bin representing a slightly different position on the corresponding lane on the road. Traffic flow direction is also assigned to each lane based upon tracking of the transform phase while the vehicle is in the radar beam.
The present invention allows dynamic adjustment to lane boundaries. Vehicle positions change over time based upon lane migration due to weather, construction, lane re-assignment as well as other traffic disturbances. The lane update process starts after the initialization is done with the continuous output of the current probability density function at regular intervals. The update process is done by effectively weighting the past and present data and then adding them together.
These and other objects and features of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth hereinafter.